Bibliographic record
Abstract
Understanding age-related macular degeneration Tunde Peto, Professor of Clinical Ophthalmology at Queen’s University Belfast, describes the symptoms, causes and treatments for age-related macular degeneration and how the prevalence of the disease could be reduced. Imagine living your life without being able to see the face of your loved ones, being able to read your phone, book a show, rebook a cancelled flight, or read the labels in the supermarket. Such tasks we do without giving these much thought until suddenly, one day, we realise that we cannot do them. Age-related macular degeneration (AMD) can lead to the loss of central vision, causing sight loss or even legal blindness. This disease is the most common cause of blindness in those over 65, (1) and while it is genetically driven in most cases, not everyone will get the disease, even if they are at risk. Its effect can be devastating, especially for those with multiple comorbidities, who have no immediate social support and for whom reading, writing, or watching television or the birds might have been the major contributor to maintaining good mental health.(2)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".